New Twists in Detecting mRNA Modification Dynamics

Modified nucleotides in mRNA are an essential addition to the standard genetic code of four nucleotides in animals, plants, and their viruses. The emerging field of epitranscriptomics examines nucleotide modifications in mRNA and their impact on gene expression. The low abundance of nucleotide modifications and technical limitations, however, have hampered systematic analysis of their occurrence and functions. Selective chemical and immunological identification of modified nucleotides has revealed global candidate topology maps for many modifications in mRNA, but further technical advances to increase confidence will be necessary. Single-molecule sequencing introduced by Oxford Nanopore now promises to overcome such limitations, and we summarize current progress with a particular focus on the bioinformatic challenges of this novel sequencing technology.

The S-phase-induced lncRNA SUNO1 promotes cell proliferation by controlling YAP1/Hippo signaling pathway

Cell cycle is a cellular process that is subject to stringent control. In contrast to the wealth of knowledge of proteins controlling the cell cycle, very little is known about the molecular role of lncRNAs (long noncoding RNAs) in cell-cycle progression. By performing genome-wide transcriptome analyses in cell-cycle-synchronized cells, we observed cell-cycle phase-specific induction of >2000 lncRNAs. Further, we demonstrate that an S-phase-upregulated lncRNA, SUNO1, facilitates cell-cycle progression by promoting YAP1-mediated gene expression. SUNO1 facilitates the cell-cycle-specific transcription of WTIP, a positive regulator of YAP1, by promoting the co-activator, DDX5-mediated stabilization of RNA polymerase II on chromatin. Finally, elevated SUNO1 levels are associated with poor cancer prognosis and tumorigenicity, implying its pro-survival role. Thus, we demonstrate the role of a S-phase up-regulated lncRNA in cell-cycle progression via modulating the expression of genes controlling cell proliferation.

Embryonic ethanol exposure alters expression of sox2 and other early transcripts in zebrafish, producing gastrulation defects

Ethanol exposure during prenatal development causes fetal alcohol spectrum disorder (FASD), the most frequent preventable birth defect and neurodevelopmental disability syndrome. The molecular targets of ethanol toxicity during development are poorly understood. Developmental stages surrounding gastrulation are very sensitive to ethanol exposure. To understand the effects of ethanol on early transcripts during embryogenesis, we treated zebrafish embryos with ethanol during pre-gastrulation period and examined the transcripts by Affymetrix GeneChip microarray before gastrulation. We identified 521 significantly dysregulated genes, including 61 transcription factors in ethanol-exposed embryos. Sox2, the key regulator of pluripotency and early development was significantly reduced. Functional annotation analysis showed enrichment in transcription regulation, embryonic axes patterning, and signaling pathways, including Wnt, Notch and retinoic acid. We identified all potential genomic targets of 25 dysregulated transcription factors and compared their interactions with the ethanol-dysregulated genes. This analysis predicted that Sox2 targeted a large number of ethanol-dysregulated genes. A gene regulatory network analysis showed that many of the dysregulated genes are targeted by multiple transcription factors. Injection of sox2 mRNA partially rescued ethanol-induced gene expression, epiboly and gastrulation defects. Additional studies of this ethanol dysregulated network may identify therapeutic targets that coordinately regulate early development.

Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data

Background

Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses.

Results

In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS).

Conclusion

Odyssey facilitates efficient and fast genome-wide association analysis automation and can go from raw genetic data to genome: phenome association visualization and analyses results in 3–8 h on average, depending on the input data, choice of programs within the pipeline and available computer resources. Odyssey was built to be flexible, portable, compatible, scalable, and easy to setup. Biologists less familiar with programing can now work hands on with their own big data using this easy-to-use pipeline.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2964-5) contains supplementary material, which is available to authorized users.

Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways

The epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) control the epithelial-to-mesenchymal transition (EMT) splicing program in cancer. However, their role in breast cancer recurrence is unclear. In this study, we report that high levels of ESRP1, but not ESRP2, are associated with poor prognosis in estrogen receptor positive (ER+) breast tumors. Knockdown of ESRP1 in endocrine-resistant breast cancer models decreases growth significantly and alters the EMT splicing signature, which we confirm using TCGA SpliceSeq data of ER+ BRCA tumors. However, these changes are not accompanied by the development of a mesenchymal phenotype or a change in key EMT-transcription factors. In tamoxifen-resistant cells, knockdown of ESRP1 affects lipid metabolism and oxidoreductase processes, resulting in the decreased expression of fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), and phosphoglycerate dehydrogenase (PHGDH) at both the mRNA and protein levels. Furthermore, ESRP1 knockdown increases the basal respiration and spare respiration capacity. This study reports a novel role for ESRP1 that could form the basis for the prevention of tamoxifen resistance in ER+ breast cancer.

Abstract P5-04-03: Aggressiveness of epithelial cancers is independent of epithelial-to-mesenchymal transition

Background: Epithelial-to-Mesenchymal Transition (EMT)is postulated to be an important step in cancer progression and controlled by multiple mechanisms including EMT transcription factors (EMT-TFs) and splicing factors such as Epithelial Splicing Regulatory Proteins (ESRP1 and ESRP2). We previously have shown that the expression of ESRP1 and ESRP2 have significantly elevated in cases with high Oncotype DX scores and in ERα-positive cells with acquired endocrine resistance (SABCS 2013). This study seeks to identify the role of EMT-TFs and ESRP1s in the determination of outcomes of patients with ER+ breast cancer.

Patients and Methods: The expression of EMT-TFs and ESRP1 was analyzed in the Affymetrix microarray and TCGA BRCA databases. Next, we generated genetically engineered knockdown models of ESRP1 to understand its functional role in endocrine resistance. We performed RNA-seq and MATS analysis to identify alternative splicing events (ASEs) between ESRP1 knockdown and control cell lines [(2C3 vs 2-control (LCC2 set) and 9C2 vs 9-control(LCC9 set)]. Validation of the ASEs was performed using a probe-based platform [Human Transcriptome Array 2.0 (HTA)] and TCGA SpliceSeq from breast tumors.

Results: High levels of ESRP1 mRNA, but not EMT-TFs, are associated with poor prognosis in human ER+ breast tumors (Affymetrix; P=2.8e-07 and TCGA; P=0.00011). Knockdown of ESRP1 in ER+ endocrine resistant breast cancer induced glandular differentiation, rather than mesenchymal features. This was associated with significant reduction in cell and tumor growth in mammary fat pad orthotopic xenograft mice models of LCC2 and LCC9. No alterations in EMT-TFs were observed in these cells. Transcriptome profiling of ESRP1 knockdown cells further revealed altered ASEs in EMT splicing gene signature, but not at the gene level. These alterations (SE-skipped exon) were further validated for ARHGEF11, ENAH, FNIP1, SCRIB, and SLK using probe based HTA platform for ESRP1 knockdown cells and TCGA-SpliceSeq ER+ BRCA tumors in ER+ ESRP1low versus ESRP1high breast tumors.

Conclusions: Our data demonstrates for the first time that high ESRP1 is associated with poor prognosis in ER+ breast cancer. Despite its involvement in regulation of EMT splicing signature, low levels (or knockdown) of ESRP1 were not associated with EMT phenotype in tumors or in endocrine-resistant ER+ cells. Taken together, our findings indicate that EMT is not important in determining prognosis in ER+ breast cancer and that ESRP1 exerts a different role in aggressive ER+ breast cancers.

Citation Format: Badve SS, Neelamraju Y, Goswami CP, Gu X, Nallamothu G, Gu Y, Vieth E, Janga SC, Ryan M, Gokmen-Polar Y. Aggressiveness of epithelial cancers is independent of epithelial-to-mesenchymal transition [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-04-03.

Abstract 1097: Knockdown of splicing factor ESRP1 affects multiple splicing factors

Introduction: Tissue-specific alternative splicing (AS) is an important mechanism for regulating gene expression. Epithelial Splicing Regulatory Proteins 1 and 2 (ESRP1 and ESRP2), two RNA-binding proteins (RBPs) that promote splicing, are potential candidates that contribute to breast cancer recurrence and resistance to therapies. In our prior studies, we have shown that ESRPs are associated with endocrine resistance. In this study, we seek to investigate the impact of the ESRP1 knockdown in endocrine resistant breast cancer cells. Methods: Expression of ESRP1 was analyzed in endocrine resistant-LCC2 and LCC9 cells. Knockdown of ESRP1 expression in LCC2 and LCC9 cells was achieved using lenti-viral based shRNA approach (Mission TRC human shRNA constructs, Sigma). We next performed paired-end RNA-sequencing of LCC2 and LCC9 cells transfected with control and two clones representative of shRNA knockdown (Illumina HiSeq platform, SeqWright Genomic Services) according to the manufacturer's instructions. After processing and quality check of raw FASTQ data (Illumina CASAVA, FASTQ and FASTX toolkits), reads were aligned, and quantified using bioinformatics software tools (Tophat/Cufflinks and Sailfish). Results: Expression of ESRP1 is significantly elevated in ERα-positive endocrine -resistant cells (LCC2 and LCC9 cells). ESRP1 knockdown was confirmed using qRT-PCR analysis in LCC2 cell lines (clone1 - 80% reduction and clone 3- 95% reduction) and in LCC9 cells (clone 2- 90% reduction and clone 3- 50% reduction). RNA-seq analysis identified 5153 differentially regulated genes between ESRP1 knockdown and control LCC2 cell lines. Of the 5153 genes, 1117 and 2109 genes were unique to clone 1 and clone 3, respectively, while 1927 genes were common in both clones. Targeted RNA-seq analysis also revealed that ESRP1 regulates the expression of several genes including other splicing factors. Furthermore, a functional enrichment analysis of these genes revealed a possible role of altering important pathways such as cell-cell adhesion and cell signaling. Additionally, it was observed that neither ESR1 nor any of its downstream genes were affected by the knockdown of ESRP1. This data was similar to that obtained in knockdown of LCC9 cells. Conclusions: ESRP1 regulates splicing of multiple genes in ER+ breast cancer. The process is independent of ER pathway. Targeting splicing could be a novel modality for treating endocrine therapy resistant breast cancer.

Citation Format: Yesim Gökmen-Polar, Yaseswini Neelamraju, Xiaoping Gu, Gouthami Nallamothu, Sarath C. Janga, Sunil Badve. Knockdown of splicing factor ESRP1 affects multiple splicing factors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1097. doi:10.1158/1538-7445.AM2015-1097

Prognostic impact of HOTAIR expression is restricted to ER-negative breast cancers

Expression of HOX transcript antisense intergenic RNA (HOTAIR), a large intergenic noncoding RNA (lincRNA), has been described as a metastases-associated lincRNA in various cancers including breast, liver and colon cancer cancers. We sought to determine if expression of HOTAIR could be used as a surrogate for assessing nodal metastases and evaluated RNA in situ hybridization (RNA-ISH) assay in a tissue microarray constructed from 133 breast cancer patients. The prognostic value of HOTAIR was further validated in large cohorts using The Cancer Genome Atlas (TCGA) breast cancer subjects. RNA-ISH analysis was successful in 94 cases (17% cases scored 0, 32.9% scored 1, 30.8% scored 2, and 19.1% scored 3). The expression of HOTAIR did not correlate with nodal metastasis regardless of the scoring intensity or with other study parameters (age, tumor size and grade, expression status). Further analysis of TCGA dataset showed that HOTAIR expression was lower in ductal carcinomas but higher in ER-negative tumors. Overexpression of HOTAIR was not associated with nodal metastases or prognosis in ER-positive patients. Its function as a poor prognostic indicator in ER-negative patients was restricted to node-positive patients. HOTAIR appears to be a marker for lymphatic metastases rather than hematogenous metastases in ER-negative patients.

Abstract 4187: Splicing factors ESRP1/ESRP2 as regulators of endocrine resistance in breast cancer

Deregulation of alternative splicing (AS) has been implicated in the development of drug resistance, tumor progression and metastasis. RNA-binding proteins (RBPs) as controllers of AS may act as master regulators of cellular behavior. Among the RBPs, we previously have shown that the expression of epithelial splicing regulatory proteins 1 and 2 (ESRP1 & ESRP2) have significantly elevated in cases with high Oncotype DX scores and in ERα-positive cells with acquired tamoxifen resistance (MCF-7/ LCC2) (SABCS 2013). This study seeks to identify genes and their splice variants that are regulated by ESRP1 & ESRP2 and could cause development of endocrine resistance. To identify alternatively spliced genes by ESRP1 & ESRP2 in endocrine resistance, we have performed a four step bioinformatics approach as follows: 1) Motif analysis to identify genes targeted by ESRP1 & ESRP2, 2) Comparison of differentially expressed genes in tamoxifen treated samples (GSE17700 and GSE17705) with genes targeted by ESRP1 & ESRP2 identified above, 3) Protein-protein interaction from BIOGRID and 4) survival data using publically available datasets (KM Plotter). Motif analysis identified 15,450 genes targeted both by ESRP1 (P<3.39e-05) and ESRP2 (P< 5.53e-05). 2212 of these genes were differentially expressed in tamoxifen treated cases. This set of genes was further categorized based on the ESRP1 & ESRP2 enhancer/ suppressor motifs ratio resulting in 1260 genes. Further collective analysis of survival data from KM plotter (P<0.001) and number of protein-protein interactions (>50) narrowed down 79 candidate genes that are associated with tamoxifen resistance. Some of the genes (AURKA, ERRB3, ESR2, EGFR, and MDM2) have been previously implicated in the de novo or acquired resistance to tamoxifen. Validation of splicing variants of these genes in human tumors is ongoing. Elevated expression of ESRP1 & ESRP2 and its target genes play an important role in the development of tamoxifen resistance and recurrence of ER+ breast cancer. ESRP1 & ESRP2 by affecting splicing have the potential to induce tamoxifen resistance via multiple genes and pathways. Targeting ESRP1 & ESRP2 may offer novel avenues for combating endocrine-resistance in breast cancer.

Citation Format: Yesim Gokmen-Polar, Yaseswini Nellamraju, Xiaoping Gu, Sarath C. Janga, Sunil S. Badve. Splicing factors ESRP1/ESRP2 as regulators of endocrine resistance in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4187. doi:10.1158/1538-7445.AM2014-4187

The RNA-binding protein Musashi1 affects medulloblastoma growth via a network of cancer-related genes and is an indicator of poor prognosis

Musashi1 (Msi1) is a highly conserved RNA-binding protein that is required during the development of the nervous system. Msi1 has been characterized as a stem cell marker, controlling the balance between self-renewal and differentiation, and has also been implicated in tumorigenesis, being highly expressed in multiple tumor types. We analyzed Msi1 expression in a large cohort of medulloblastoma samples and found that Msi1 is highly expressed in tumor tissue compared with normal cerebellum. Notably, high Msi1 expression levels proved to be a sign of poor prognosis. Msi1 expression was determined to be particularly high in molecular subgroups 3 and 4 of medulloblastoma. We determined that Msi1 is required for tumorigenesis because inhibition of Msi1 expression by small-interfering RNAs reduced the growth of Daoy medulloblastoma cells in xenografts. To characterize the participation of Msi1 in medulloblastoma, we conducted different high-throughput analyses. Ribonucleoprotein immunoprecipitation followed by microarray analysis (RIP-chip) was used to identify mRNA species preferentially associated with Msi1 protein in Daoy cells. We also used cluster analysis to identify genes with similar or opposite expression patterns to Msi1 in our medulloblastoma cohort. A network study identified RAC1, CTGF, SDCBP, SRC, PRL, and SHC1 as major nodes of an Msi1-associated network. Our results suggest that Msi1 functions as a regulator of multiple processes in medulloblastoma formation and could become an important therapeutic target.

Network-based function prediction and interactomics: the case for metabolic enzymes

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases.